Favouring and encouraging the use of public transport or other alternatives to private car is an environmental challenge. It requires understanding how people experience on a daily basis the infrastructures and transport services determined by local authorities. Everyday, experiences about bottleneck and overcrowded bus are shared between users of the social network Twitter.
The aim of the project is, on the basis of tweet interpretation protocols, to discover the daily experience of transport users leaving in the employment area of Luxembourg.
In order to assess the representativeness, a survey on a sample of them will be conducted to collect their socio-demographic data, their schedules, their tracks and their tweets.
These data will feed several probabilistic neural network models to infer users trajectory and type of declared experience. This range of calibrated models will allow to determine an audience of each declared experience over the employment area of Luxembourg. In order to improve the results, a community of users will be created to reveal more precisely and systematically their daily experiences. Comparing these results with operating data and mobility plans, it will be possible to assess if collecting the daily experience of transport users over a large territory can be a new and rich source of information for formulating mobility plans.
Thus, this research project will provide new kinds of information for stakeholders of Luxembourg to monitor daily transport experience of users over a large territory such as the employment area of Luxembourg.